Quality Control of Microwave Equipment for Tissue Imaging
Bibliographic record
Abstract
While the development of microwave imaging technology for biomedical applications has been ongoing for many years, no clinical devices are currently in use. A major challenge is achieving data quality that would ensure adequate image resolution for the specific diagnostic application. Imaging systems are typically designed with a theoretical resolution limit in mind, which is rarely achieved in practice due to measurement uncertainties, background clutter, and system noise. Uncertainties and background clutter are particularly prominent in medical diagnostic imaging. This paper proposes a method for data quality assessment of an experimental imaging system that aims at a specific image resolution. It utilizes two measurements, one of a uniform background medium and one of the same medium with a small scattering probe embedded within it. The probe's size and permittivity reflect the desired application-specific resolution. The method extracts the system point-spread function (PSF) from the two measurements and computes the PSF contrast-to-noise ratio. A case study is presented, demonstrating the quality control protocol and its ability to identify datasets of inadequate quality and provide an evaluation metric. The protocol also highlights possible sources of error and enables data filtering that increases significantly the reconstructed image quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".